Questionnaire phase 2, very rough draft

I'm posting this just to give you an idea of what the phase 2 survey might look like. It is strongly based on steve's threshold framework, maybe too much detail but can always be pruned back some. So there are plenty of errors...but let me know do you like the general direction, or do you have some broad brush comments at this point?

Another thing:

now that you have a rough draft of phase 2, and at least an idea of what it will look like.... I would like more finalized comments on phase 1, and the two Wupatki ecosite s&t models. Basically I want to know am I clear by you guys to initiate phase 1 of the surveys? I've got 3 respondents willing to do it now, which is enough to get going. If so I'll give them a last edit and send them off.

let me know-M

{like the phase one survey, here I will have a paragraph-length summary of key concepts which will be addressed in the survey. This will be a simplified, bare-bones, version of Steve’s threshold framework}

Please review the revised state-and-transition model. These have been reviewed and revised according.

{Insert S&T Model here}

Please complete the following questionnaire. There are a small number of required questions (indicated by ***). We cannot use your survey response if any of these are omitted. The other questions are not required but we value your thoughts, and appreciate as many answers as you are willing to give.

1.Please identify the at-risk (of transition to degraded states) phase and vulnerable phase within the reference condition. [Currently the model structure implies that the grazed grassland phase is at-risk].

1. The current model is correct, ungrazed grasslands are not at-risk, and grazed grasslands are at-risk.
2. Both phases are at-risk.
3. None of the phases are at-risk.

2. We believe that the resistant properties of the non-vulnerable phase result from a negative feedback wherein grass cover has a positive effect on fire-return interval, and fire return interval has a negative impact in tree or shrub establishment, which in turn has a positive effect on grass dominance. Do you agree or disagree, and what are the other major feedback mechanisms that confer resistance to transition in the non-vulnerable state.

a. agree, no additional feedbacks.

b. disagree, additional feedbacks (please describe)

c. agree, additional feedbacks (please describe)

d. disagree, no additional feedbacks.

The grass-fire-shrub negative feedback is the only major negative feedback conferring resistance to the non-vulnerable phase of the reference state.

b. The grass-fire-shrub negative feedback is important but there are other key feedbacks (please describe

Now please consider some questions regarding specific transitions in the model:

Transition 7.

3. An at-risk phase, can transition to another state if appropriate triggers are engaged. We currently believe that, transition 7 has two possible triggers: fire suppression, and fuels reduction by grazing animals. Indicate if you tend to believe this, and indicate any other triggers which may be important in initiating transition 7.

a. agreed, these are the two main ones

b. disagree, one or both of these is not a trigger of this transition (please explain briefly)

c. agreed, these are two important triggers, but there are additional ones (explain)

d. disagree, one or both of these is not a trigger of this transition (explain), AND there are additional triggers (explain).

4. A transition can be loosely divided into a preventative threshold, or restoration threshold. The preventative portion is the part where resilience properties of the at-risk phase can promote ecosystem recovery if the trigger(s) identified in question 4 are disengaged. Regarding transition 7, we believe that the primary property conferring resilience is the resprouting ability of the native rhizomatous grasses. Do you agree, and are there others we should be aware of.

a. agree, no other

b. disagree, others include:

c) agree, others include:

d) disagree, no others

5. In the preventative threshold phase, there may be management practices which will allow resiliency mechanisms to work, returning the ecosystem back into the reference or potential state. We believe these practices include cessation of grazing if active, and not suppressing grassland wildfires when possible [if they can sustain themselves]. Do you agree, and are there others?

a) agree, no other

b) disagree, others include:

c) agree, others include:

d) disagree, no others

6. ***We have compiled a list of monitorable, measureable indicators which may be informative in determining if a threshold sequence is in the preventative or restoration phase. Some are part of the National Park Service’s vital signs and are currently being monitored, others are not but perhaps should be refined and added. These pertain to the amount of above ground fine fuels available to sustain wildfire, and the connectivity of these variables. They are:

6-1.Total basal cover including litter

6-2. Average length of bare patches

6-3. Average length of combustible patches (dashed line indicates how length would be measured)

[fig here]

Do you agree that these are valuable indicators, and which others should we consider.

a) agree, no other

b) disagree (explain), others include:

c) agree, others include:

d) disagree (explain), no others

7. ***For each indicator identified in 6, including any written in by you please estimate the value below which the resiliency mechanisms (e.g. resprout of rhizomatous grasses) alone would be insufficient to allow a return to the reference state is a trigger were disengaged. If you listed new indicators make similar estimates for them using the other boxes.

[We do not expect respondents to know the true answer, rather we are asking them to make educated guesses. Make the best guess you can. To help you answer, current average basal cover of plants and litter (ungrazed since 1996) is about 8-10. Average length of bare patches is xxx, and we have no information on average length of combustible patches.]

1. 6-1. (answer in percentage)
2. 6-2 (answer in cm)
3. 6-3 (answer in cm)
4. Other 1 (respondent identified)
5. Other 2 (respondent identified)
6. Other 3 (respondent identified)

8) ***Please estimate your overall confidence in the estimates provided in question 7.

[Please answer on a subjective scale of 0 – 100% certainty. Enter any value in this range. To help you answer: 0% means “It’s anyone’s guess, these estimates are no better than any other estimates”, 50% means “Because these estimates are reasonable I would tend to believe it until evidence to the contrary is presented”, 100% means “The estimates are so well-supported by evidence and accumulated knowledge, that I am certain it is correct.”]

9) If your level of confidence in any particular estimate differs from the value above please estimate your confidence for that estimate in the appropriate box. In case you are estimating a confidence in a state or transition suggested by you in questions 2 and 4, please use the “other” boxes to identify it.

[If you don’t provide an answer here, we will assume that your confidence in all ases is the same as question 8]

7. 6-1. (answer in percentage)
8. 6-2 (answer in cm)
9. 6-3 (answer in cm)
10. Other 1 (respondent identified)
11. Other 2 (respondent identified)
12. Other 3 (respondent identified)

***10)Please take a moment to think of any scientist or other person, who is to your knowledge the best qualified to estimate the values you estimated in question 7. This person could be yourself, or any other person. “Best” qualified may or may not mean highly qualified; sometimes no one is highly qualified. Now, in the hypothetical scenario that this person had made these question 7 estimates, using all of the data, knowledge and experience available to them, estimate how much confidence you would have that it is the correct model of the most important ecosystem states, processes and dynamics of the ecosite in question.

[Please answer on a subjective scale of 0 – 100% certainty. Enter any value in this range. To help you answer: 0% means “It’s anyone’s guess, any person could produce equally good or bad estimates”, 50% means “Because these estimates are reasonable I would tend to believe it until evidence to the contrary is presented”, 100% means “The estimates are so well-supported by evidence and accumulated knowledge, that I am certain it is correct.]

11) If during a threshold sequence, an ecosystem state surpasses these values it may move into the restoration portion. Negative feedback mechanisms may become dominant again, stabilizing the ecosystem into a new degraded state, in the case of the transition, the savannized state. At this point, only active restoration practices can achieve a return to reference conditions. We believe that applicable active restoration practices include those which remove the invading woody plants. Do you agree, and are there others?

a) agree, no other

b) disagree (explain), others include:

c) agree, others include:

d) disagree (explain), no others

[repeat process for each key management-relevant threshold]

Thank you for taking time out of your schedule to complete this questionnaire, we greatly appreciate your input!

Please let us know if you would be willing to do the same type of survey for additional ecosites which might fall under your area of expertise.

Steve's conceptual thresholds framework

Conceptual Threshold Framework

We assembled a conceptual framework to help focus thinking about the dynamics of ecological sites and ecological thresholds (Fig. 1). We integrated published frameworks to take advantage of different perspectives, but largely relied on the resilience-based state-and transition framework of Briske et al. (2008). Associated with our conceptual framework is a narrative template to aid in capturing current understanding of the key features of reference and alternative states, to identify plausible transition triggers, and to document possible resilience properties of states (Table 1).

The conceptual framework starts the reference or native state of an ecological site, and community phases of the state (Fig. 1 - 1). Native or reference conditions typically are the desired state for conservation management. Negative feedbacks confer system resilience and maintain the community phases within the state. For instance, a negative feedback that inhibits shrub dominance in grasslands is the interaction between amount of grass cover and fire return interval. Given sufficient grass cover, wildfire events are often and large enough to inhibit the establishment of shrubs across a grasslands. Resilience is defined as the amount of change required to overcome stabilizing, negative feedbacks. Phases comprising the natural range of reference conditions differ in their vulnerability to disturbances and stressors. Phases with diminished structure or partially impaired functioning are more vulnerable (e.g., 1-III in Fig.1). In the absence of severe disturbances and stressors, however, a vulnerable phase may develop to a more resistant phase (e.g., 1-I in Fig. 1). Alternatively, the intensity of a driver or stressor and the temporal coherence of these change agents can exceed the resilience of a phase, and initiate a threshold crossing with equal development of negative and positive feedbacks mechanisms. Narrative information about the reference state includes a description of the indicators of non-vulnerable phases (key compositional, structural, and functional properties), the feedbacks that confer resilience, and the indicators of vulnerable or at-risk phase(s).

A threshold crossing occurs due to biotic or abiotic triggers which can span spatial scales from site level (e.g., trampling) to regional (e.g., climate). A key feature emphasized in the framework is the recognition that a single driver or stressor (trigger) may initiate a threshold crossing, or temporal order or spatial convergence of multiple triggers may be critical for a threshold crossing. For example, drought or excessive livestock grazing may not significantly alter a site, but the combination of these may precipitate soil erosion and the eventual development of alternative states. For each trigger or combination of triggers, subsequent alternative states will differ. For all triggers, the following generalized threshold progression is representative, but the sequence and types of alternative states will differ. The narrative requires a description of the triggers known or hypothesized to initiate threshold crossings.

The threshold progression is characterized by increasing dominance of positive feedbacks, and changes in compositional and functional properties. Over time there may be a substantive change in the structural properties of a site followed by a loss of reference-condition species and the establishment of other native or exotic species (3 in Fig. 1). Inherent in this progression is the continual loss of properties of the reference condition. The early portion of a threshold progression is referred to as preventative thresholds (Bestelmeyer, 2006), indicating that residual components of reference conditions and resilience still exist. Properties of resilience include those, for example, which promote soil nutrient and water retention, and the regeneration of native plants. Many alternative states can occur in this threshold progression phase, with some becoming stable alternative states. Occurrence of alternative stable states as well as the direction and rate of threshold progression is mediated by feedback dominance, and the influence of drivers and stressors. With intervention (facilitating practices (Brandon B. cit)– 5 in Fig. 1) these states may be reverted to reference conditions. This may require the removal of the dominant species of the post-threshold state or simply the removal of the change agent (e.g., social trailing, livestock grazing). The degree of residual reference conditions and resilience influences the probability of reversibility. The narrative for this includes procedures likely to result in the reversal to pre-threshold conditions. The emphasis on preventive thresholds is to highlight the fact that a threshold crossing doesn’t equate to degraded conditions that can never be restored to pre-threshold conditions.

Continued increase in the dominance of negative feedbacks that stabilize alternative states can proceed to a point where features of the reference condition are effectively no longer present, and negative feedback mechanisms maintain the stability of a degraded state (4 in Fig. 1). The pattern and process thresholds leading to this phase determine the degraded state. Thus, for an ecological site, multiple stable, degraded states are possible. Active restoration methods (accelerating practices – 6 Fig. 1) are required to revert to reference conditions (Brandon.B. cit). The energy and costs to revert a degraded state may be prohibitive from a management perspective. Also, reversibility may not be biologically possible due to the extinction of native biota (i.e., species, genomes), and the loss of inherent properties necessary to support reference conditions. The narrative for degraded states includes a description of structural and compositional properties of plausible states, properties conferring resilience of these states, and potential restoration actions, including the probability of restoration success. In some cases, complete restoration to native conditions may never be possible due to the extinction of inherent system properties. Thus, a description of the most likely conditions that can be restored is included.

Post-threshold states have consequences for ecosystem services and resource management (7 – Fig. 1). These consequences increase along the gradient from preventative to restoration thresholds. Degraded states especially may no longer afford provision of services such as water (amount and quality), livestock forage production, or desirable recreational opportunities, and may no longer support the biodiversity of native systems. Consequences to resource management include the cost of restoration actions, where possible. The narrative for consequences of threshold crossings includes a description of the impacted ecosystem services, and the practicality and costs of restoration actions.




Table 1. Narrative guide to capture the essential properties and dynamics of reference states and of alternative states resulting from a threshold crossing (see Fig. 1). Section 1 is repeated for each community phase of reference conditions. Sections 2-4 are repeated for each possible threshold crossing and threshold progression.
Ecological Site name
1. Reference State; Community Phase_________.
Structural properties
Functional properties
Negative feedback mechanisms
Inherent properties conferring resilience (properties that tend not to change; e.g., soil properties)
Dynamic properties
conferring resilience (properties with the potential for large change; e.g., vegetation structure and composition)
Vulnerability to threshold crossing
Possible management-assessment point (process, pattern characteristics for early-warning of an impending threshold crossing)
2. Threshold Crossing
Transition triggers
(list biotic, abiotic trigger(s), and temporal order & spatial convergence properties)
3. Preventive Threshold
Transition triggers leading to this state from other alternative states (if applicable)
Likelihood of occurrence
Structural properties
Functional properties
Negative feedback mechanisms
Positive feedback mechanisms
Condition of the Inherent properties conferring resilience in the reference state
Condition of the Dynamic properties conferring resilience in the reference state
Mechanism(s) of reversibility (facilitating practices)
Probability of reversibility (probability of complete restoration or probability of restoring near-similar reference conditions and attributes of these conditions)
Consequences of this state to ecosystem services, resource management
Consequences of this state to resource management
4. Restoration Threshold
Transition triggers leading to this state from other alternative states (if applicable)
Likelihood of occurrence
Structural properties
Functional properties
Negative feedback mechanisms
Positive feedback mechanisms
Inherent properties conferring resilience
Dynamic properties conferring resilience
Mechanism(s) of reversibility (accelerating practices)
Probability of reversibility (probability of complete restoration or probability of restoring near-similar reference conditions and attributes of these conditions)
Consequences of this state to ecosystem services
Consequences of this state to resource management

Expert opinion survey Phase 1 V2

General update: I realized the comments setting were preventing people without google IDs from leaving comments. This is corrected. Now you can select "anonymous" and leave a comment (but sign it within the comment please).

On this questionnaire, I think I have addressed Mark's concerns here, and have brought the question more in line with the guiding questions accompanying the old draft of Steve's Threshold Attribute table. He has a new draft which I'll post when I receive it.

Remember this questionnaire is to make sure we have a reasonable model. Phase 2 will be about estimating critical thresholds, and will be focused on the key management-oriented transitions. So so more stuff from the threshold attribute table will show up there rather than here. This 2 step process is reasonable to me because, a repondent may identify a new threshold in phase 1. The other respondents cannot comment on it, becasue they don't know the suggestions of other people. So we need to incorporate the suggestions into a new model, and then ask people to talk about trnasitons from the new model. make sense?

Steve, I especially would value your thoughts about whether this questionnaire lines up with your vision.



Background:
State-and-transition models are an increasingly common conceptual method of organizing knowledge of successional dynamics in ecosystems. Unlike older successional theories they allow for multiple successional pathways, do not assume a single “climax” and do not assume that successional change is reversible. They assume that a given ecosystem might be capable of shifting among multiple stable states, when a specific “trigger” leads to a transition. The model seeks to identify the primary stable states, and key phases within those states, and the transitions among them. States and phases are defined by structural properties such as dominant plants and other biota or biodiversity, and functional properties/processes such as frequent fire return intervals or high soil aggregate stability. The properties of states and phases may be dynamic (e.g. plant cover & community composition) or inherent (e.g. climate, soil properties, landscape physiography), and may confer resistance (the ability to maintain the same set of structural/functional properties, i.e. not change) or resilience (the ability to experience change but return to the original set of structural/functional properties) to ecosystem change. (examples: 1) a dynamic property conferring resistance might be biological crust cover which decreases erodibility, 2) an inherent property conferring resistance might be high rock cover which prevents various soil disturbances from resulting in erosion, 3) a dynamic property conferring resilience might be rhizomatous grass cover: grazing animals remove biomass, and removal of the grazers when the forage quantity becomes poor results in a recovery of biomass due to resprout from tillers, 4) an inherent property conferring resilience might be a climate which favors a wet season after the grazing season, and facilitates recovery. Phases within states are different varieties of a state, and transition from one phase to another tend to be fairly common, easily triggered, and often reversible. Less degraded states tend to be characterized by negative feedbacks which confer resilience. In contrast, transitions among stable states, may be abrubt, and may be irreversible because of mechanisms which maintain the ecosystem in that state. Every transition has an underlying trigger, for example introduction of grazing. The trigger initiates a change in structural or functional properties of the ecosystem (e.g. loss of vegetative cover, and disruption of soil surface aggregates), which may engage new processes which bring about a transition to a new state (e.g. a positive feedback loop wherein lack of vegetation and soil aggregates leads to erosion which prevents the recolonization of vegetation or creation of aggregates). Such positive feedbacks which led to a transition can also maintain degraded states.


The best way to understand a state-and transition model is to study one. Please review the following model and answer the questionnaire below it. Questions marked *** are required, we cannot use your response if any of these are omitted. The other responses are optional, and much appreciated.

[insert model here]

***1) Please identify any states or phases which should be omitted from the state-and-transition model.
Multiple choice, e.g…..
a. P1
b. P2
c. S1
d. S1P1
e. S1P2
f. S2
g. S3
h. None, all should be retained

2) Please identify any states or phases which are currently not in the model, but should be added to the state and transition model.
[Please briefly list structural properties like dominant species or overall vegetative cover (whatever you feel is important to mention), and functional properties & processes such as fire return intervals, or low soil stability. When you list properties please think about and indicate if they are dynamic or inherent, and if they contribute to the resistance or resilience of the state or phase. Please indicate any feedback mechanisms which tend to maintain these states. Let us know about appropriate literature if available.]

***3) Please identify any transitions which should be omitted from the state-and-transition model.

a. T1
b. T2
c. T3
d. T4
e. T5
f. T6
g. T7
h. T8
i. T9
j. None, all should be retained

3) Please identify any transitions which are currently not in the model but should be added to the state and transition model.
[For each addition provide, the starting state and ending state for which the transition applies. Identify plausible trigger mechanisms. Also please provide a brief explanation of the process that brings about the transition, e.g. fire, insect outbreak, drought, grazing. If reasonable touch upon the dominant scale of the trigger mechanism, and the importance of temporal convergence and order with other mechanisms (e.g. simultaneous drought and grazing may function as a trigger when either alone do not).]


***5)Please estimate your overall confidence that a new revised model which takes into account your proposed modifications is the correct model of the most important ecosystem states, processes and dynamics of the ecosite in question.

[Please answer on a subjective scale of 0 – 100% certainty. Enter any value in this range. To help you answer: 0% means “It’s anyone’s guess, this model is no better than any other model”, 50% means “Because this model is reasonable I would tend to believe it until evidence to the contrary is presented”, 100% means “The model is so well-supported by evidence and accumulated knowledge, that I am certain it is correct.”]

6)If your level of confidence in any particular state or transition differs from the value above please estimate your confidence for that model component in the appropriate box. In case you are estimating a confidence in a state or transition suggested by you in questions 2 and 4, please use the “other” boxes to identify it.

[If you do not provide answers to 6 we will assume they are the same as the answers to 5 in all cases.]


a. P1
b. P2
c. S1
d. S1P1
e. S1P2
f. S2
g. S3
h. other state or phase
i. other state of phase
k. T1
l. T2
m. T3
n. T4
o. T5
p. T6
q. T7
r. T8
s. T9
t. other transition
u. other transition


***7)Please take a moment to think of any scientist or other person, who is to your knowledge the best qualified to develop a state-and-transition model for this ecosite. This person could be yourself, or any other person. “Best” qualified may or may not mean highly qualified; sometimes no one is highly qualified. Now, in the hypothetical scenario that this person had prepared a state-and-transition model for this ecosite using all of the data, knowledge and experience available to them, estimate how much confidence you would have that it is the correct model of the most important ecosystem states, processes and dynamics of the ecosite in question.

[Please answer on a subjective scale of 0 – 100% certainty. Enter any value in this range. To help you answer: 0% means “It’s anyone’s guess, any person could produce an equally good or bad model”, 50% means “Because this model is reasonable I would tend to believe it until evidence to the contrary is presented”, 100% means “The model is so well-supported by evidence and accumulated knowledge, that I am certain it is correct.]

8.) Who is the best qualified person (from question 7) to develop this state-and transition model? This response will help us ensure we have contacted all of the right people.

9.) How much time did you spend on this? This is important for improving future iterations of this questionnaire.

10.) How would you improve this survey?

Thank you for taking time out of your schedule. We will use your comments to revise our model, and will contact you again about phase 2 of the survey.

Draft expert opinion Survey

In stage 1 of the expert opinion gathering, the part where we calibrate the state-and-transition model and estimate how well supported it is, this is what I would envision presenting to repondents.

In some cases there might be ecosite-specific questions to help resolve places where I am unsure, e.g. "Do you believe that S1 and S3 are distinct states?" or something like that, but this would be the general template of questions.

*** = required questions

I think that a person might spend less than 10 minutes on the required questions, but could spend up to 20 minutes more on the more open-ended questions.

what do you think?? any additions needed. I think number 8 is important, but is it obnoxious?



***1)Please identify any states or phases which should be omitted from the state-and-transition model.

Multiple choice, e.g…..
a. P1
b. P2
c. S1
d. S1P1
e. S1P2
f. S2
g. S3
h. None, none all should be retained

2)Please identify any states or phases which are currently omitted, but should be added to the state and transition model. Please briefly list dominant species, dominant processes and key characteristics, and appropriate references if they are available.

***3)Please identify any transitions which should be omitted from the state-and-transition model.

Multiple choice, e.g….
a. T1
b. T2
c. T3
d. T4
e. T5
f. T6
g. T7
h. T8
i. T9

4)Please identify any transitions which are currently omitted but should be added to the state and transition model. For each addition provide, the starting state and ending state for which the transition applies. Also please provide a brief explanation of the process that brings about the transition, e.g. fire, insect outbreak, drought, grazing.

5) Please identify other changes you think should be made to the draft model, if any.


***6)Please estimate your overall confidence that a new model which takes into account your proposed modifications is the correct model of the most important ecosystem states, processes and dynamics of the ecosite in question.

[Please answer on a subjective scale of 0 – 100% certainty. Enter any value in this range. To help you answer: 0% means “It’s anyone’s guess, this model is no better than any other model”, 50% means “Because this model is reasonable I would tend to believe it until evidence to the contrary is presented”, 100% means “The model is so well-supported by evidence and accumulated knowledge, that I am certain it is correct.”]

7)If your level of confidence in any particular state or transition differs from the value above please estimate your confidence for that model component in the appropriate box. In case you are estimating a confidence in a state or transition suggested by you in questions 2 and 4, please use the “other” boxes to identify it.

If you do not provide answers to 7 we will assume they are the same as the answers to 6 in all cases.

****8) Please take a moment to think of any scientist or other person, who is to your knowledge the best qualified to develop a state-and-transition model for this ecosite. This person could be yourself, or any other person. “Best” qualified may or may not mean highly qualified. Now, in the hypothetical scenario that this person had prepared a state-and-transition model for this ecosite using all of the data, knowledge and experience available to them, estimate how much confidence you would have that it is the correct model of the most important ecosystem states, processes and dynamics of the ecosite in question.

[Please answer on a subjective scale of 0 – 100% certainty. Enter any value in this range. To help you answer: 0% means “It’s anyone’s guess, any person could produce an equally good or bad model”, 50% means “Because this model is reasonable I would tend to believe it until evidence to the contrary is presented”, 100% means “The model is so well-supported by evidence and accumulated knowledge, that I am certain it is correct.]

Limey Uplands (Wupatki) Second Draft

Limey Uplands (Wupatki)

States [Due to a history of volcanism and long-term human occupation, appropriate reference vegetative communities in Wupatki should be defined with care. Here we emphasize what is likely possible under management scenarios today. Paleoecological studies have described reference communities from a variety of time periods. Some are not possible under current conditions. States are divided into pre-historic and historic-modern (manageable) to make this distinction]:

Pre-historic States (not possible under current management scenarios):

P1: Pre-eruption.An original vegetation state under approximately modern climate regimes is difficult to reconstruct, and sketchy at best. Based upon packrat midden and palynological analysis, it is likely that grasslands were common (Cinnamon 1988). Plant macrofossils demonstrate that Juniperus monosperma was present (Cinnamon 1988; but not confirmed by Ironside 2006) and also seems to suggest a greater prevalence of Stipa hymenoides, and the grass Enneapogon desvauxii, and the shrub Artemisia bigelovii. Other common species, also present today, include Hillaria jamesii, Chrysothamnus nauseosum, and Opuntia erinaceae. Notably Bouteloua spp-a current co-dominant- were only sparsely represented. Because biological crusts have very low potential on basaltic soils on the Colorado Plateau, they were likely a very minor ecosystem component (Bowker and Belnap 2007). Frequent ground fires (15-20 yr return interval) likely maintained grasslands and limited tree and shrub establishment (Cinnamon 1988, Hassler 2006).

P2: Post-eruption, post occupation. State P1 experienced two drastic changes which almost certainly led to rapid transitions. First the Sunset Carter eruption deposited up to several centimeters of volcanic ash and cinder possibly killing vegetation. Cinder deposition is thought to have improved water infiltration dynamics, and likely strongly altered hydrology (Sullivan and Downum 1991). Within a few decades an approximately century-long occupation of dry-farming societies ensued. These are though to have been abandoned due to declining soil nutrient stores (Sullivan and Downum 1991). Midden and palynological data indicates shifts in relative abundance of some taxa (Cinnamon 1988) including a decrease in Enneapogon, Artemisia bigelovii, and Stipa hymenoides, an apparent arrival of Stipa comata, and no major change in Hillaria jamesii. Frequent ground fires (15-20 yr return interval) likely maintained grasslands and limited tree and shrub establishment (Cinnamon 1988, Hassler 2006).

Current and recent states:

S1: Current potential Grassland

S1Phase1: Rested Grassland. Prior to the inititiation of grazing the area had over 500 years to recover from agricultural disturbance. We know little about this period, except that midden analysis suggests that relative abundance of plant macrofossils does not appear to have changed much over this time. Presumably cover increased. A range assessment of the region from the early 1970’s provides some seemingly overoptimistic estimates of “climax” cover at 60-90%. The assessment also suggests that a greater prevalence of Stipa species, and a lesser prevalence of Hillaria jamesii, Bouteloa gracilis (Doughty 1971). These assertions were based upon general knowledge of the range assessor rather than specific knowledge of the ecosystem. The greater importance of Heterostipa comata in the past is confirmed by midden evidence, but middens suggest that Hillaria is not a product of increasing dynamics under grazing. Rather it has been a major community component for centuries. The supposedly frequent ground fire interval (Cinnamon 1988, Hassler 2006) would tend to favor the rhizomatous species such as Hillaria and Bouteloua (Jameson 1962, Ford 1999).

Given this somewhat conflicting body of evidence, we can say that, if we accept the hypothesis that frequent ground fires were common, then rhizomatous grasses dominated. At least in some years cover and connectivity of grass and litter patches would have had to have been sufficient to carry a burn. Jameson (1962) estimated that a 1956 wildfire had 13% cover pre-burn, although these estimates could be biased because they were taken post-burn in nearby unburned areas. We can take this as a reasonable first approximation of a minimum cover to sustain fire. Finally, there may have been a greater prevalence of Heterostipa comata, though this tussock grass is not likely to have dominated.

S1Phase2: Grazed ecosystem.

Around 1900, grazing was introduced (Jameson 1962), and its intensity peaked early in that century. Based upon general knowledge of behavior of common plant species under grazing, palatable grasses such as Heterostipa comata would be expected to decrease, and unpalatable species such as Guiterrizia and Chrysothamnus or grazing tolerant species such as Bouteloua gracilis might increase. Grazing decreases the standing biomass of fine fuels and their connectivity, decreasing the susceptibility of this system to fire. This in turn allows more Juniperus recruits to invade. Invasion by Salsola may also be possible at this stage.

S2 variant b: Denuded state. If heavy grazing continues unabated, a transition to a severly denuded state is possible. This state is characterized by greater cover of bare ground, an overall decrease in vegetation to very low levels, and an increase in the relative prevalence of unpalatable species (Chryosthanmus, Opuntia, Guiterrizia) and possibly invaders (Salsola). Although the cinder-covered surface and generally flat slopes of this ecosite lend it low erosion potential, such a reduction in vegetation cover could conceivably initiate erosion since water-stable aggregate structure is very poor (Generally <2>

S2 variant j: Denuded state, juniper overstory. If junipers were allowed to establish and grow to adulthood (see S3), and heavy grazing reinsitituted, we would expect a degradation of the soil surface similar to C4b, with an overstory of high-lined juniper trees. Similar states can be observed outside of park boundaries to the north closer to Cameron.

S3: Savannization. To date, limey uplands have been less susceptible to juniper encroachment than surrounding areas but it is clear that the prevalence of juniper in increasing in the grasslands and former grasslands of Wupatki (Cinnamon 1988, Hassler 2006, Parker 2009, Ironside 2006) A comparison of basalt soils to limestone soils indicated that tree growth rate or density did not differ, but average age of establishment did occurred later on basaltic soils (Hassler 2006). There is sufficient rooting depth, but perhaps the soil texture challenges the junipers ability to colonize delaying the process (Bowker et al. unpublished). Simulation modeling based upon 20^th

century climates regimes indicate that limey uplands have a high probability of invasion (Ironside 2006). If grazing keeps fines fuels low, fire cannot cull colonizing junipers making savannization likely. It is not known if this can proceed to a woodland state like some nearby areas on different soils, but this possibility is not favored by future climate projections thus is not considered here (Ironside 2006).

Photos (Jameson 1962) demonstrate both a denuded phase in 1906, and savannization in 1960 in the general area of Antelope Prairie.

Transitions:

T1: Cinder and deposition due to volcanism beginning in 1064 likely initially destroys much vegetation, but enhances infiltration and water retention dynamics of soils. From ~ 1200 - 1300 Shortly afterward, agricultural societies practiced dryfarming, exploiting the properties of the cinder soils.

T2. Long term rest (centuries) allowed succession to proceed to a fire-maintained grassland.

T3. (Syndromes B2, B5) Introduction of grazing reduces palatable herbage in favor of unpalatable vegetation, reduction of fine fuels eliminates frequent fire.

T4. (Reversal of syndromes B2, B5) Adequate rests allows reestablishment of fine fuels and fire.

T5. (Syndromes B6, A1, A4) High intensity grazing reduces plant cover to the point that these systems are susceptible to water erosion.

T6. (Reversal of B6, A1, A4) Some fairly well-denuded systems eventually recovered on a time scale of decades, exhibiting an unusual resiliency. T5 and T6 likely obey a threshold dynamic.

T7. (Syndromes B4) Elimination of fire also eliminates the major force preserving the grass life-form dominance. Trees and shrubs are able to colonize.

T8. (Syndrome B5) Global change-type drought leads to death of woody plants and reversion to a grass-dominated ecosystem. This transition has not yet been observed in Wupatki, but has been nearby outside of Sunset Crater (Bowker et al. 2010, Gitlin et al. 2006).

T9. Identical to T5 except that grazing occurs after Juniper establishment.

Useful indicators and rationale (bold italics indicates most important under current management scenarios):

I1. Fuel load, inclusive of grasses, dead herbaceous and woody material, and litter T3, T4. Fuel load could be estimated based on measured cover and height of plants, with a one-time calibration versus biomass. These indicators will help inform whether fire is possible.

I2. Connectivity of fine fuels could be measured in multiple ways. The mean length of bare areas would be one useful easy measure. Also, mean length of fine fuel islands (prep figure) could be key. T3, T4. Visual ordinal indices (Leonard 2009) and bare ground and rock cover (Knapp and Keeley 2006), though such indices leave much room for improvement. These indicators will help inform whether fire is possible.

I3. Woody plant density, e.g. number of individuals per area. Frequency may be an acceptable proxy. T3, T4, T7, T8. Because woody plants are long-lived and slow growing, the number of individuals may be more informative than cover in determining risk of savannization or other woody plant dominance

I4. Cover of bare ground. T6. Of the controls on erosion in this ecosite, only vegetation cover and its converse bare ground are likely to be dynamic. These will determine susceptibiltiy to erosion, because vegetation cover modulates erositivity.

I5. Proportion of live to dead junipers may come to be relevant during global change type droughts, but is not currently useful to monitor frequently.

Data Availability: Sparse

Hassler (2006) likely conducted some sampling of Juniperus density, growth rate, and fire mortality on Limey uplands

DeCoster and Swan (2009) summarizes the first years of the I & M program and contains the most purposefully collected dataset for Limey Uplands. May capture unexploited recovery from fire gradients (1 plot in 1995 north fire, 3-4 plots in the 2002 Antelope fire). Contains good information on vegetation structure and ground cover but lacks indices of connectivity of fine fuels, or average bare ground length. Sites may all be in more or less the same state.

Bowker and Belnap (2007) sampled biological soil crust cover and soil stability on limey uplands assumed to be at potential condition.

Miller et al. (2007) sampled 7 plots on limey uplands.

Recommendations for I & M program:

Consider low intensity (perhaps one time only) sampling of key variables in plots outside of monument boundaries on Limey Uplands. This will help confirm that the state-and-transition model is reasonable, and may provide a data-based confirmation of estimated threshold points in key monitoring variables. Examples include savannized limey uplands south of the monument boundary on the Wukoki lava flow, and various stages of grazed systems, and denudation on Antelope Prairie north of the monument boundary.

Refine and implement monitoring data which relate to fuel load and connectivity, the manageable aspects of fire susceptibility. Consider pursuing a site-specific fire susceptibility model.

Utilize the different fire histories in analysis of the current monitoring plots. Opportunistically monitor plots in future fires.

Literature Cited

Cinnamon, S.K. 1988. The vegetation community of Cedar Canyon, Wupatki National Monument as influenced by prehistoric and historic environmental change. Master’s Thesis, NAU.

Doughty, J.W. 1971. Soil survey and range site and condition inventory. Wupatki National Monuments, Arizona. A special report. USDA SCS.

DeCoster, J. K., and M. C. Swan. 2009. Integrated upland vegetation and soils monitoring for WupatkiNational Monument: 2008 summary report. Natural Resource Data Series NPS/SCPN/NRDS—2009/022. National Park Service, Fort Collins, Colorado.

Knapp, E.E., and J.E. Keeley. 2006. heterogeneity in fire severity in early and late season prescribed burns in a mixed conifer forest. International Journal of Wildland Fire 15: 37-45.

Hassler, F. 2006. Dynamics of juniper invaded grasslands and old growth woodlands at Wupatki National monument, Northern Arizona, USA. Master’s Thesis, Northern Arizona University.

Leonard, S. 2009. Predicting sustained fire spread in Tasmanian Native grasslands. Environmental Management 44: 430-440.

Miller, ME, Witwicki, DL, Mann, RK, Tancreto, NJ. 2007. Field evaluation of smapling methods for long-term monitoring of upland ecosystems on the Colorado Plateau. USGS Open File Report 2007-1243.

Bowker, M.A. and Belnap, J. 2007. Spatial Modeling of Biological Soil Crusts to Support Land Management Decisions: Indicators of Range Health and Conservation–restoration Value Based Upon the Potential Distribution of Biological Soil Crusts in Montezuma Castle, Tuzigoot, Walnut Canyon, and Wupatki National Monuments, Arizona.

Bowker, MB, Munoz, AA, Martinez, T., Lau, MK. 2010. Rare drought-induced mortality of juniper: edaphic and climatic stressors promote hydraulic failure. unpublished manuscript.

Gitlin, AR, Sthultz, CM, Bowker, MA, Stumpf, S., Paxton, K.L., Kennedy, K., Munoz, A., Bailey, J.K., Whitham, TG. 2006. Mortality gradients within and among dominant plant populations as barometers of ecosystem change during extreme drought. Conservation Biology 20: 1477-1486.

Ford, PL. 1999. Response of buffalograss (Buchloe dactyloides) and blue grama (Bouteloua gracilis) to fire. Great Plains Research 9: 261-76.

Ironside, K. 2006. Climate change research in national parks; paeloecology, policy, and modeling the future. Master’s Thesis, Northern Arizona University.

Jameson, D.A. 1962. Effects of Burning on a Galleta-Black Grama Range Invaded by Juniper. Ecology 43: 760-763.

Sullivan, AP, Downum, CE. 1991. Aridity, activity, and volcanic ash agriculture: a study of short-term prehistoric cultural-ecological dynamics. World Archaeology 22: 271-287.

Bowker, M.A. and Belnap, J. 2007. Spatial Modeling of Biological Soil Crusts to Support Land Management Decisions: Indicators of Range Health and Conservation–restoration Value Based Upon the Potential Distribution of Biological Soil Crusts in Montezuma Castle, Tuzigoot, Walnut Canyon, and Wupatki National Monuments, Arizona.

Sandstone Uplands (Wupatki) V1.

click on image for bigger version


Sandstone Uplands (Wupatki)

States

P1. Pre-eruption. Vegetation was likely shrub-dominated, with Ephedra viridis, Atriplex confertifolia being the best represented species in packrat middens in the Wupatki Basin in general (Ironside 2006). Grasses are a conspicuously absent component. Soil surface characteristics suggest potential for some degree of biological soil crust development. Soil crust may have been well-developed enough to decrease erodibility of the soil surface. A model based on the assumption that Wupatki soil surfaces are adequately rested, and therefore are at their potential regarding soil crusts [a questionable assumption], estimated potential cover of late successional crust elements at <>

P2. Post eruption and occupation. State P1 experienced two drastic changes which almost certainly led to rapid transitions. First the Sunset Carter eruption deposited up to several centimeters of volcanic ash and cinder possibly killing vegetation. Cinder deposition is thought to have improved water infiltration dynamics, and likely strongly altered hydrology (Sullivan and Downum 1991). Biological soil crust potential, previously low to moderate, would have been strongly constricted by locally varying amounts of cinder occupying soil surface area and reducing available habitat (Bowker and Belnap 2007). Within a few decades an approximately century-long occupation of dry-farming societies ensued. These are thought to have been abandoned due to declining soil nutrient stores (Sullivan and Downum 1991).

S1. Reference Shrubland/ grass component. After over 500 years of recovery from cinder deposition and impacts associated with occupation by agricultural societies, the ecosystem presumably recovered. There are no pre-grazing samples from Sandstone Uplands, however several packrat middens were analyzsed on shale uplands normally about 100m distant. Ephedra viridis, Atriplex canescens, and Fallugia paradoxa are especially well represented (Ironside 2006). The same evidence suggests that several grass species were primarily recent arrivals in the last 500 years, perhaps favored by the cinder deposition. The assumed grass dominance of “climax” communities in early soil and range site surveys is not supported by any other evidence (Doughty 1971).

S2. Denuded. Introduction of grazing would have reduced overall vegetation cover, and would tend to increase the relative abundance of unpalatable shrubs over more palatable grasses. Hoof action would increase erodibility by disruption of soil aggregates. Examination of times series photos seems to indicate that vegetation cover in some areas of the Wupatki Basin were markedly denuded around the turn of the 20^th century and well into that century (Unknown 1890-1920, Unknown 1905, Muench 1950-1970). Other historical photos indicate the loss of vegetation in the Wupatki area in general (e.g. Cedar Canyon, Antelope Prairie, Jameson 1962). Invasion by Salsola is possible and common, but no current evidence indicates that it is likely to be dominant.

Wukoki Ruin 1905. Area very close to ruin is unvegetated today, but photograph indicates very little vegetation in the landscape. The vegetation in the surrounding area has since recovered.

Heiser Spring 1890-1920. Unclear if animals are in a very large enclosure, or in open range. Corner of corral to left of bottom photo suggests they are not corralled.

S3. Post-grazing shrubland. Within the national monument, this is the typical state (Decoster and Swan 2009, Hansen and Thomas 2004) as grazing was reduced after the 1920’s and ceased, with the exception of occasional trespass, in 1989. We are uncertain as to whether this constitutes a separate state from S1. It is a shrubland with a grass component, with an overall species list not unlike S1. The primary difference seems to be the dominance of Artemisia filifolia, and the lower importance of Atriplex cancescens (Hansen and Thomas 2004, DeCoster and Swan 2009). Artemisia filifolia was not even detected in packrat middens near sandstone upland sites prior to grazing, whereas Atriplex canescens was the most commonly represented plant fossil (Ironside 2006). It is not known if this is a spatial artifact due to proximity of the middens to Deadman Wash, which is today richer in Atriplex. These midden locations average about 100m distant from Sandstone Uplands, thus this is plausible since this is near the upper limits of packrat foraging (Cinnamon 1988). However it is curious that the current dominant is not represented at all in nearby middens, while another cinder-loving species was well represented (Fallugia paradoxa). Doughty (1971) notes the presence of this species, but provides a community composition list (apparently ordered) topped by Chrysothamnus nauseosum and Ephedra in the early 1970s, possibly an earlier successional sere on the way to eventual Artemisia dominance. Current total cover is typically 5 – 10%, and it is not known if this is comparable to or lower than S1.

S4. Severely eroded. This state is largely theoretical, since most previously denuded areas seem to be vegetated today. If denudation and intense grazing pressure were to persist, and abiotic processes, e.g. erosion, dominated, then vegetation, soil fertility, and biotic activity would be unlikely to recover. Cinders help reduce runoff, but in the exposed soil surface, water-stable aggregation is very low, typically scoring in the lowest two categories using the Herrick soil stability test (Bowker and Belnap 2007). Doughty (1971) also indicates a greater erosion risk with overgrazing, and steeper slopes. Unchecked erosion would not allow establishment of vegetation, and the lack of vegetation would increase erosivity and erodibility, perpetuating erosion.

Transitions

T1: Cinder and deposition due to volcanism beginning in 1064 likely initially destroys much vegetation and biotic crusts, but enhances infiltration and water retention dynamics of soils. From ~ 1200 - 1300, agricultural societies practiced dryfarming, exploiting the properties of the cinder soils.

T2. Long term rest (centuries) allowed succession to proceed.

T3. Introduction of grazing reduces overall vegetative cover, proportionally reduces palatable herbage in favor of unpalatable vegetation. Animal vectors can facilitate Salsola invasion.

T4. Cessation of grazing allows recovery of vegetative cover.

T5? Shrubland succession proceeds arriving at original condition. [much uncertainty here regarding the distinctness of S1 and S3, proposed successional pathway is conjecture]

T6. Active erosion and poor hydrological control prevent reestablishment of vegetation, creating a positive feedback.

Useful indicators and rationale:

For the most part, the I&M program is monitoring the right indicators.
I1. Vegetative community structure. T3, T4, T5. Will help determine if current communities are dynamic or stable, and will help determine their trajectory if dynamic.

I2. Total vegetative and ground cover, and cover of bare gound. T3, T5, T6. Of the controls on erosion in this ecosite, only vegetation cover and its converse bare ground are likely to be dynamic. These would help detect grazing-induced changes in modifiers of erosivity, and dynamics in these variables after cessation of grazing (the current management scenario).

I3. Erosional features. T3, T4, T6. As stated above, when plant cover is sparse there is a greater probability of erosion. Detection/quantification of direct evidence of erosion, e.g rills, gullies, terracettes, pedestelling, scalding could be informative about advancement or cessation of these processes, especially in areas thought to be currently degraded. Wupatki-specific indicators may seek to detect coppicing-like behavior in the eolian redistribution of cinders.



Data Availability: Sparse
Ironside (2006) sampled post-occupation, pre-grazing packrat middens in Deadman Wash, mapped very near to or within Sandtone Uplands units.

Hansen, et al. (2004) created a classification of plant communities, and mapped them using remote sensing and ground truthing.

DeCoster and Swan (2009) summarizes the first years of the I & M program and contains the most purposefully collected dataset for Sandstone Uplands. Contains good information on vegetation structure and ground cover, but lacks data on erosional features (e.g. rills, gulleys, terracettes). There are 3 years of data but sites may all be in basically the same state, and not very dynamic.

Bowker and Belnap (2007) sampled biological soil crust cover, soil stability, and surface roughness on sandstone uplands assumed to be at potential condition.

Several photosets (Muench 1950-1970, Unknown 1936, Unknown 1890-1920, Euler 1936-1950, and Parke 1960-1970), provide a very incomplete image of what vegetation looked like at various time periods. Most photos are not focused on landscapes, but some information can be extracted.

Recommendations for I & M program:
Consider low intensity (perhaps one time only) sampling of key variables in plots outside of monument boundaries on Sandstone Uplands, which are still undergoing grazing. This will help confirm that the state-and-transition model is reasonable, and may provide a data-based confirmation of estimated threshold points in key monitoring variables.
Consider refining and implementing monitoring data which relate to dynamic surface erosional features, such as sand deposition, rills, gullies, pedestelling, scalding, and terracettes.


Literature Cited

Bowker, M.A. and Belnap, J. 2007. Spatial Modeling of Biological Soil Crusts to Support Land Management Decisions: Indicators of Range Health and Conservation–restoration Value Based Upon the Potential Distribution of Biological Soil Crusts in Montezuma Castle, Tuzigoot, Walnut Canyon, and Wupatki National Monuments, Arizona.

Cinnamon, S.K. 1988. The vegetation community of Cedar Canyon, Wupatki National Monument as influenced by prehistoric and historic environmental change. Master’s Thesis, NAU.

Doughty, J.W. 1971. Soil survey and range site and condition inventory. Wupatki National Monuments, Arizona. A special report. USDA SCS.

Hansen, M., J. Coles, and K. Thomas. 2004. Vegetation of Wupatki National Monument. U.S. Geological Survey, Forest and Rangeland Ecosystem Science Center, Colorado Plateau Field Station, Flagstaff, Arizona. Final report to the USGS-NPS Vegetation Mapping Program.

Ironside, K. 2006. Climate change research in national parks; paeloecology, policy, and modeling the future. Master’s Thesis, Northern Arizona University.

Jameson, D.A. 1962. Effects of Burning on a Galleta-Black Grama Range Invaded by Juniper. Ecology 43: 760-763.

Meunch, J. 1950 – 1970. Photographs 1950-1970. NAU Digital Photo Archives.

Unknown. 1936. Wukoki ruin. NAU Digital Photo Archives.

Unknown. 1890 – 1920. People, Animals and activities at Heiser Spring near Wupatki National Monument, Arizona. NAU Digital Photo Archives.

Euler, RC. 1936 – 1950. Non-pueblo Indians: Navajo. NAU Digital Photo Archives.

Parke, H. 1960-1970. National Parks: Rainbow Bridge, Saguaro, Sunset Crater, Timpanogos Cave, Tonto, Tumacacori, Tuzigoot, Walnut Canyon, White Sands, Wupatki, Yellowstone, and Yosemite. NAU Digital Photo Archives

Sullivan, AP, Downum, CE. 1991. Aridity, activity, and volcanic ash agriculture: a study of short-term prehistoric cultural-ecological dynamics. World Archaeology 22: 271-287.